Remark: The set of policies—and therefore learning algorithms—available to an agent depends on the set of locally observable histories, which itself depends on the observation channel \(( \mathbb {Y}, \lbrace  \varphi ^i \rbrace _{i \in \mathcal {N}})\) . In this paper, our focus is on independent learners, which are learners that do not use the joint action information in their learning algorithms, either because they are intentionally ignoring this information or because they are unable to observe the joint actions. Here, we have chosen to incorporate this constraint into the information structure. Moreover, to underscore the importance of learning in our study, we also do not assume that the players know the cost function \(c\) . Instead, we assume only that they receive feedback costs in response to particular system interactions. These assumptions on the information structure resemble those of other work on independent learners, e.g. [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, and can be contrasted with work on joint action learners, where the locally observable history variables also include the joint action history.
